Image data sets are in general generated by image generation devices. These image generation devices are, for example, mammography scanners, computed tomography scanners, magnetic resonance imaging scanners and ultra sound scanners, which are, in particular, used for diagnostic purposes. The generated image data sets are often transferred to marks generation devices, like a computer-aided-detection device (CAD device), for determining marks indicating certain locations within the image data sets. For example, in the case of a medical image data set, the CAD device can determine marks indicating locations within the image data set, which are suspicious of showing cancer. In this case, a user, like a radiologist, could examine the locations within the image data set indicated by the marks, in order to determine, whether cancer is present or not.
Such an image data set comprises often a large amount of marks, whereby a viewer, for example, a radiologist, can be confused and might overlook important or suspicious marks.
It is known to visualize the marks within the image data set with variable size. It is, for example, known to visualize marks of higher importance, e.g. of higher suspiciousness of marking cancer, with a larger size than marks having a smaller importance in order to draw the attention of a user to the most important marks. But there are still a lot of marks present in the image data set having different sizes, wherein a user, like a radiologist, can still be confused. Furthermore, if the computer program, which is used to determine the importance of a mark, like a CAD computer program of a CAD device, does not correctly determine the importance of a mark, this mark is visualized with a smaller size and the probability of overlooking an important mark is even increased.